`cramersV.Rd`

These functions compute the point estimate and confidence interval for Cramer's V.

cramersV(x, y = NULL, digits = 2) # S3 method for CramersV print(x, digits = x$input$digits, ...) confIntV(x, y = NULL, conf.level = 0.95, samples = 500, digits = 2, method = c("bootstrap", "fisher"), storeBootstrappingData = FALSE) # S3 method for confIntV print(x, digits = x$input$digits, ...)

x | Either a crosstable to analyse, or one of two vectors to use to generate that crosstable. The vector should be a factor, i.e. a categorical variable identified as such by the 'factor' class). |
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y | If x is a crosstable, y can (and should) be empty. If x is a vector, y must also be a vector. |

digits | Minimum number of digits after the decimal point to show in the result. |

… | Any additional arguments are passed on to the |

conf.level | Level of confidence for the confidence interval. |

samples | Number of samples to generate when bootstrapping. |

method | Whether to use Fisher's Z or bootstrapping to compute the confidence interval. |

storeBootstrappingData | Whether to store (or discard) the data generating during the bootstrapping procedure. |

A point estimate or a confidence interval for Cramer's V, an effect size to describe the association between two categorical variables.

### Get confidence interval for Cramer's V ### Note that by using 'table', and so removing the raw data, inhibits ### bootstrapping, which could otherwise take a while. confIntV(table(infert$education, infert$induced));#> Cramér's V 95% confidence interval (point estimate = .18): #> Using Fisher's z: [.06; .3]